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ردیف | عنوان | نوع |
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1 |
Annealing-based Quantum Computing for Combinatorial Optimal Power Flow
محاسبات کوانتومی مبتنی بر بازپخت برای جریان قدرت بهینه ترکیبی-2022 This paper proposes the use of annealing-based
quantum computing for solving combinatorial optimal power
flow problems. Quantum annealers provide a physical com-
puting platform which utilises quantum phase transitions to
solve specific classes of combinatorial problems. These devices
have seen rapid increases in scale and performance, and are
now approaching the point where they could be valuable for
industrial applications. This paper shows how an optimal power
flow problem incorporating linear multiphase network modelling,
discrete sources of energy flexibility, renewable generation place-
ment/sizing and network upgrade decisions can be formulated as
a quadratic unconstrained binary optimisation problem, which
can be solved by quantum annealing. Case studies with these
components integrated with the ieee European Low Voltage
Test Feeder are implemented using D-Wave Systems’ 5,760
qubit Advantage quantum processing unit and hybrid quantum-
classical solver. Index Terms— Distribution Network | D-Wave | Electric Vehicle | Optimal Power Flow | Power System Planning | Quantum Annealing | Quantum Computing | Smart Charging. |
مقاله انگلیسی |
2 |
Influence of wire electrical discharge machine cutting parameters on the magnetization characteristics of electrical steel laminations
تاثیر پارامترهای برش دستگاه تخلیه الکتریکی سیم بر ویژگی های مغناطش ورقه های فولادی الکتریکی-2021 This paper presents the influence of wire electrical discharge machine (WEDM) cutting parameters on the
magnetization characteristics of electrical steel. The demand for electrical steel keeps increases because
of the high demand of electrical motor and transformers for electric vehicle and smart grid applications.
The magnetization characteristics of the laminated steel used in these machines have dominates the performance of these machines. The cuttings methods and cutting parameters have huge role in deciding the
magnetization characteristics. In this work, WEDM is used to cut the electrical steel in to small pieces.
The cutting parameters such as current, feed rate and on-time of WEDM are varied by keeping two of
these parameters fixed and one varied. From the electrical steel sheet, 18 sample small electrical steel
pieces are obtained at various cutting parameters of WEDM. The magnetic field – magnetization characteristics are obtained using vibrating sample magnetometer (VSM). This analysis shows that higher current, lower on-time and higher feed rate is providing higher magnetization.
Keywords: Wire electrical discharge machine | Vibrating sample magnetometer | Electrical steel | Cutting parameter | Magnetization |
مقاله انگلیسی |
3 |
Pricing and free periodic maintenance service decisions for an electric-and-fuel automotive supply chain using the total cost of ownership
قیمت گذاری و تصمیمات خدمات تعمیر و نگهداری دوره ای رایگان برای یک زنجیره تامین خودروی الکتریکی و سوختی با استفاده از کل هزینه مالکیت-2021 Although subsidies have had significant impacts on the electric vehicle (EV) market share, many governments have planned to eliminate subsidies. There is a concern that unsubsidized EVs reduce the EV market share, significantly. However, purchasing an EV instead of a fuel vehicle (FV) might impose a lower total cost of ownership (TCO) on customers, depending on their vehicle usage. In this case, supply chains could optimize their decisions considering which vehicle is affordable for each customer class from the view of TCO. This study investigates optimal pricing and free periodic maintenance service (FPMS) decisions in a two-stage electric-and- fuel automotive supply chain, considering TCO to estimate vehicle market shares under customer classification with different vehicle usage patterns. Two bi-level models are developed and solved through Karush-Kuhn- Tucker equations and a reformulation-and-decomposition algorithm. Sensitivity analyses are performed considering various scenarios on energy prices and ownership periods. Results indicate that the high-usage customers are more likely to purchase an EV if the ownership period is the same for all classes. However, if low-usage customers keep the vehicle for a longer period than the others, they are more likely to purchase an EV. Both providing FPMSs by the manufacturer instead of the retailer and increasing the fuel price over time with a higher rate, compared with the electricity price and the inflation rate, improve the EV market share and reduce the total fuel consumption and emissions. Investment to produce EVs is not economical for a high price of electricity while having low fuel prices. Keywords: Electric vehicles | Total cost of ownership | Automotive supply chain | Pricing decision | Bi-level programming | Decomposition algorithm |
مقاله انگلیسی |
4 |
Optimal production and pricing strategies in auto supply chain when dual credit policy is substituted for subsidy policy
استراتژی های تولید و قیمت گذاری بهینه در زنجیره تأمین خودکار هنگامی که سیاست اعتبار دوگانه جایگزین سیاست یارانه شود-2021 The Chinese government has proposed a dual credit policy (DCP) as a substitute for electric vehicle (EV) subsidies, which fluctuates the auto market. To investigate the policy substitution influences for the production and pricing strategies, we use Stackelberg game paradigms to model a two-stage auto supply chain. The manufacturer regulated by the DCP produces both EV and internal combustion engine vehicles (ICEV). The retailer sells them to heterogeneous consumers. By backward induction, the optimal pro- duction and pricing strategies are derived for the subsidy policy only (scenario B) and with a joint subsidy policy and DCP (scenario DS). Our findings show, 1) different with only one case in scenario B, the manufacturer and the retailer have three corresponding optimal production and pricing strategies in scenario DS, according to the manufacturer’s Corporate Average Fuel Consumption credit (CAFC credit);2) the demand for the ICEV may also decline like EV as the subsidies are phased out in scenario DS when the manufacturer’s CAFC credit is in balance case; 3) the changes of DCP rules may have different effects on the optimal production and pricing strategies in different CAFC cases.© 2021 Elsevier Ltd. All rights reserved. Keywords: Auto supply chain | Stackelberg game paradigms | Dual credit policy | Subsidy policy | Production and pricing |
مقاله انگلیسی |
5 |
Buyer selection and service pricing in an electric fleet supply chain
انتخاب خریدار و قیمت گذاری خدمات در زنجیره تأمین ناوگان الکتریکی-2021 Much attention has been focused on supplier selection in operations. There has been less research on the
supplier selecting buyers in a two-echelon supplier-buyer chain, which we study for downstream taxicab
vehicle fleets. We consider the problem of pricing infrastructure services by an electric vehicles (EVs) service provider (SP), which determines the group of taxicab companies (TCs) that will adopt EVs. We study
SP’s pricing decisions in a decentralized supply chain under a general infrastructure cost function, multiple TCs, and symmetric information. We extend the modeling to the case with (i) endogenous demand
and EV-taxicab end-consumer pricing and (ii) asymmetric information between SP and TC. We analyze
the factors that influence SP’s profits and the set of participating TCs who adopt EVs. We find that when
the fleet size of TCs increases, SP prefers to serve more low-mile TCs than the high-mile TCs and even
removes some high-mile TCs in exchange for low-mile TCs, where low and high-miles correspond to average miles driven in a time period (shift). When the coefficient of variation of miles driven increases,
SP prefers to serve more high-mile TCs than the low-mile TCs. In general, the set of TCs that adopt EVs
cannot be simply characterized using inputs such as average miles driven by different TCs. This study
provides a modeling framework and managerial implications for TC selection and pricing contracts by an
EV infrastructure service provider. Keywords: Supply chain management | Buyer selection | Electric Vehicles | Service pricing | Submodular infrastructure cost |
مقاله انگلیسی |
6 |
Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle
استراتژی مدیریت انرژی مبتنی بر یادگیری تقویتی عمیق قانون برای خودروی الکتریکی هیبریدی تقسیم برق-2020 The optimization and training processes of deep reinforcement learning (DRL) based energy management
strategy (EMS) can be very slow and resource-intensive. In this paper, an improved energy management
framework that embeds expert knowledge into deep deterministic policy gradient (DDPG) is
proposed. Incorporated with the battery characteristics and the optimal brake specific fuel consumption
(BSFC) curve of hybrid electric vehicles (HEVs), we are committed to solving the optimization problem of
multi-objective energy management with a large space of control variables. By incorporating this prior
knowledge, the proposed framework not only accelerates the learning process, but also gets a better fuel
economy, thus making the energy management system relatively stable. The experimental results show
that the proposed EMS outperforms the one without prior knowledge and the other state-of-art deep
reinforcement learning approaches. In addition, the proposed approach can be easily generalized to other
types of HEV EMSs. Keywords: Energy management strategy | Hybrid electric vehicle | Expert knowledge | Deep deterministic policy gradient | Continuous action space |
مقاله انگلیسی |
7 |
Deep reinforcement learning based energy management for a hybrid electric vehicle
مدیریت انرژی مبتنی بر یادگیری تقویت عمیق برای یک وسیله نقلیه الکتریکی هیبریدی-2020 This research proposes a reinforcement learning-based algorithm and a deep reinforcement learningbased
algorithm for energy management of a series hybrid electric tracked vehicle. Firstly, the powertrain
model of the series hybrid electric tracked vehicle (SHETV) is constructed, then the corresponding
energy management formulation is established. Subsequently, a new variant of reinforcement learning
(RL) method Dyna, namely Dyna-H, is developed by combining the heuristic planning step with the Dyna
agent and is applied to energy management control for SHETV. Its rapidity and optimality are validated
by comparing with DP and conventional Dyna method. Facing the problem of the “curse of dimensionality”
in the reinforcement learning method, a novel deep reinforcement learning algorithm deep Qlearning
(DQL) is designed for energy management control, which uses a new optimization method
(AMSGrad) to update the weights of the neural network. Then the proposed deep reinforcement learning
control system is trained and verified by the realistic driving condition with high-precision, and is
compared with the benchmark method DP and the traditional DQL method. Results show that the
proposed deep reinforcement learning method realizes faster training speed and lower fuel consumption
than traditional DQL policy does, and its fuel economy quite approximates to global optimum.
Furthermore, the adaptability of the proposed method is confirmed in another driving schedule. Keywords: Hybrid electric tracked vehicle | Energy management | Dyna-H | Deep reinforcement learning | AMSGrad optimizer |
مقاله انگلیسی |
8 |
Cooperative control strategy for plug-in hybrid electric vehicles based on a hierarchical framework with fast calculation
استراتژی کنترل تعاونی برای وسایل نقلیه برقی هیبریدی پلاگین بر اساس یک چارچوب سلسله مراتبی با محاسبه سریع-2020 Developing optimal control strategies with capability of real-time implementation for plug-in hybrid
electric vehicles (PHEVs) has drawn explosive attention. In this study, a novel hierarchical control
framework is proposed for PHEVs to achieve the instantaneous vehicle-environment cooperative control.
The mobile edge computation units (MECUs) and the on-board vehicle control units (VCUs) are included
as the distributed controllers, which enable vehicle-environment cooperative control and reduce the
computation intensity on the vehicle by transferring partial work from VCUs to MECUs. On this basis, a
novel cooperative control strategy is designed to successively achieve the energy management planned
by the iterative dynamic programming (IDP) in MECUs and the energy utilization management achieved
by the model predictive control (MPC) algorithm in the VCU. The performance of raised control strategy
is validated by simulation analysis, highlighting that the cooperative control strategy can achieve superior
performance in real-time application that is close to the global optimization results solved offline. Keywords: Cooperative control strategy | Hierarchical framework | Iterative dynamic programming (IDP) | Model predictive control (MPC) | Plug-in hybrid electric vehicles (PHEVs) |
مقاله انگلیسی |
9 |
A real-time blended energy management strategy of plug-in hybrid electric vehicles considering driving conditions
یک استراتژی مدیریت انرژی ترکیبی از زمان واقعی خودروهای برقی پلاگین با توجه به شرایط رانندگی-2020 In this study, a blended energy management strategy considering influences of driving conditions is
proposed to improve the fuel economy of plug-in hybrid electric vehicles. To attain it, dynamic programming
is firstly applied to solve and quantify influences of different driving conditions and driving
distances. Then, the driving condition is identified by the K-means clustering algorithm in real time with
the help of Global Positioning System and Geographical Information System. A blended energy management
strategy is proposed to achieve the real-time energy allocation of the powertrain with incorporation
of the identified driving conditions and the extracted rules, which includes the engine starting
scheme, gear shifting schedule and torque distribution strategy. Simulation results reveal that the proposed
strategy can effectively adapt to different driving conditions with the dramatic improvement of
fuel economy and the decrement of calculation intensity and highlight the feasibility of real-time
implementation Keywords: Plug-in hybrid electric vehicles | Energy management strategy | Global optimization | Driving condition | Equivalent driving distance coefficient |
مقاله انگلیسی |
10 |
Online energy management strategy of fuel cell hybrid electric vehicles based on rule learning
استراتژی مدیریت انرژی آنلاین از وسایل نقلیه برقی هیبریدی سلول سوختی بر اساس یادگیری قانون-2020 In this paper, a rule learning based energy management strategy is proposed to achieve preferable energy
consumption economy for fuel cell hybrid electric vehicles. Firstly, the optimal control sequence of
fuel cell power and the state of charge trajectory of lithium-ion battery pack during driving are derived
offline by the Pontryagin’s minimum principle. Next, the K-means algorithm is employed to hierarchically
cluster the optimal solution into the simplified data set. Then, the repeated incremental pruning
to produce error reduction algorithm, as a propositional rule learning strategy, is leveraged to learn and
classify the underlying rules. Finally, the multiple linear regression algorithm is applied to fit the
abstracted parameters of generated rule set. Simulation results highlight that the proposed strategy can
achieve more than 95% savings of energy consumption economy, solved by Pontryagin’s minimum
principle, with less calculation intensity and without dependence on prior driving conditions, thereby
manifesting the feasibility of online application. Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Hierarchical clustering | Rule learning |
مقاله انگلیسی |